A systematic review of the prediction of hospital length of stay:Towards a unified framework

Hospital length of stay of patients is a crucial factor for the effective planning and management of hospital resources. There is considerable interest in predicting the LoS of patients in order to improve patient care, control hospital costs and increase service efficiency. This paper presents an extensive review of the literature, examining the approaches employed for the prediction of LoS in terms of their merits and shortcomings. In order to address some of these problems, a unified framework is proposed to better generalise the approaches that are being used to predict length of stay. This includes the investigation of the types of routinely collected data used in the problem as well as recommendations to ensure robust and meaningful knowledge modelling. This unified common framework enables the direct comparison of results between length of stay prediction approaches and will ensure that such approaches can be used across several hospital environments. A literature search was conducted in PubMed, Google Scholar and Web of Science from 1970 until 2019 to identify LoS surveys which review the literature. 32 Surveys were identified, from these 32 surveys, 220 papers were manually identified to be relevant to LoS prediction. After removing duplicates, and exploring the reference list of studies included for review, 93 studies remained. Despite the continuing efforts to predict and reduce the LoS of patients, current research in this domain remains ad-hoc; as such, the model tuning and data preprocessing steps are too specific and result in a large proportion of the current prediction mechanisms being restricted to the hospital that they were employed in. Adopting a unified framework for the prediction of LoS could yield a more reliable estimate of the LoS as a unified framework enables the direct comparison of length of stay methods. Additional research is also required to explore novel methods such as fuzzy systems which could build upon the success of current models as well as further exploration of black-box approaches and model interpretability

Determining Factors Influencing Length of Stay and Predicting Length of Stay Using Data Mining in the General Surgery Department

Background: Length of stay is one of the most important indicators in assessing hospital performance. A shorter stay can reduce the costs per discharge and shift care from inpatient to less expensive post-acute settings. It can lead to a greater readmission rate, better resource management, and more efficient services. Objective: This study aimed to identify the factors influencing length of hospital stay and predict length of stay in the general surgery department. Methods: In this study, patient information was collected from 327 records in the surgery department of Shariati Hospital using data mining techniques to determine factors influencing length of stay and to predict length of stay using three algorithms, namely decision tree, Naïve Bayes, and k-nearest neighbor algorithms. The data was split into a training data set and a test data set, and a model was built for the training data. A confusion matrix was obtained to calculate accuracy. Results: Four factors presented: surgery type (hemorrhoid), average number of visits per day, number of trials, and number of days of hospitalization before surgery; the most important of these factors was length of stay. The overall accuracy of the decision tree was 88.9% for the training data set. Conclusions: This study determined that all three algorithms can predict length of stay, but the decision tree performs the best

Developing Predictive Models for Risk of Postoperative Complications and Hemodynamic Instability in Patients Undergoing Surgery

Patients undergoing high-risk surgeries are often at higher risk of developing hemodynamic instability during surgery resulting in poor postoperative outcomes. This is usually associated with significantly increased postoperative morbidity and mortality, which therefore makes the early identification of these critical events and those patients at risk of postoperative complications crucial. With these motivations in mind, we first created a large deidentified research dataset of surgical case medical records from University of California, Irvine Medical Center (UCIMC) matched with physiological waveforms as well as intermittent vital sign values, lab values, and ventilator settings. To our knowledge, such a dataset does not currently exist for the intraoperative environment. We hope that creating a such a dataset will allow for advances in machine learning for intraoperative care. Using medical data from UCLA, we have developed deep neural network models to classify the risks of postoperative mortality, acute kidney injury, and reintubation utilizing readily available intraoperative information. Our risk scores were compared to currently commonly used risk indices ASA and Surgical Apgar as well as logistic regression. While the deep neural network models performed better than the risk scores and logistic regression, clinicians require additional information to assess what led to increased risk of complications. To address this, we also assessed the use of generalized additive neural networks (GANNs) to create a graphical look at how different features contributed to the risk of in hospital mortality. Finally, we were also interested in predicting critical intraoperative events to allow for time for the clinician to avoid such events. We focused on intraoperative hypotension as it is easier to define and has been shown to lead to increased risk of acute kidney injury, stroke, and myocardial injury. For the hypotension prediction models, we looked at the arterial pressure waveform and EMR data as inputs. Overall, these aims address a gap in current clinical decision guidance and support to reduce adverse events during surgery as well complications after

The use of knowledge discovery databases in the identification of patients with colorectal cancer

Colorectal cancer is one of the most common forms of malignancy with 35,000 new patients diagnosed annually within the UK. Survival figures show that outcomes are less favourable within the UK when compared with the USA and Europe with 1 in 4 patients having incurable disease at presentation as of data from 2000.Epidemiologists have demonstrated that the incidence of colorectal cancer is highest on the industrialised western world with numerous contributory factors. These range from a genetic component to concurrent medical conditions and personal lifestyle. In addition, data also demonstrates that environmental changes play a significant role with immigrants rapidly reaching the incidence rates of the host country.Detection of colorectal cancer remains an important and evolving aspect of healthcare with the aim of improving outcomes by earlier diagnosis. This process was initially revolutionised within the UK in 2002 with the ACPGBI 2 week wait guidelines to facilitate referrals form primary care and has subsequently seen other schemes such as bowel cancer screening introduced to augment earlier detection rates. Whereas the national screening programme is dependent on FOBT the standard referral practice is dependent upon a number of trigger symptoms that qualify for an urgent referral to a specialist for further investigations. This process only identifies 25-30% of those with colorectal cancer and remains a labour intensive process with only 10% of those seen in the 2 week wait clinics having colorectal cancer.This thesis hypothesises whether using a patient symptom questionnaire in conjunction with knowledge discovery techniques such as data mining and artificial neural networks could identify patients at risk of colorectal cancer and therefore warrant urgent further assessment. Artificial neural networks and data mining methods are used widely in industry to detect consumer patterns by an inbuilt ability to learn from previous examples within a dataset and model often complex, non-linear patterns. Within medicine these methods have been utilised in a host of diagnostic techniques from myocardial infarcts to its use in the Papnet cervical smear programme for cervical cancer detection.A linkert based questionnaire of those attending the 2 week wait fast track colorectal clinic was used to produce a ‘symptoms’ database. This was then correlated with individual patient diagnoses upon completion of their clinical assessment. A total of 777 patients were included in the study and their diagnosis categorised into a dichotomous variable to create a selection of datasets for analysis. These data sets were then taken by the author and used to create a total of four primary databases based on all questions, 2 week wait trigger symptoms, Best knowledge questions and symptoms identified in Univariate analysis as significant. Each of these databases were entered into an artificial neural network programme, altering the number of hidden units and layers to obtain a selection of outcome models that could be further tested based on a selection of set dichotomous outcomes. Outcome models were compared for sensitivity, specificity and risk. Further experiments were carried out with data mining techniques and the WEKA package to identify the most accurate model. Both would then be compared with the accuracy of a colorectal specialist and GP.Analysis of the data identified that 24% of those referred on the 2 week wait referral pathway failed to meet referral criteria as set out by the ACPGBI. The incidence of those with colorectal cancer was 9.5% (74) which is in keeping with other studies and the main symptoms were rectal bleeding, change in bowel habit and abdominal pain. The optimal knowledge discovery database model was a back propagation ANN using all variables for outcomes cancer/not cancer with sensitivity of 0.9, specificity of 0.97 and LR 35.8. Artificial neural networks remained the more accurate modelling method for all the dichotomous outcomes.The comparison of GP’s and colorectal specialists at predicting outcome demonstrated that the colorectal specialists were the more accurate predictors of cancer/not cancer with sensitivity 0.27 and specificity 0.97, (95% CI 0.6-0.97, PPV 0.75, NPV 0.83) and LR 10.6. When compared to the KDD models for predicting the same outcome, once again the ANN models were more accurate with the optimal model having sensitivity 0.63, specificity 0.98 (95% CI 0.58-1, PPV 0.71, NPV 0.96) and LR 28.7.The results demonstrate that diagnosis colorectal cancer remains a challenging process, both for clinicians and also for computation models. KDD models have been shown to be consistently more accurate in the prediction of those with colorectal cancer than clinicians alone when used solely in conjunction with a questionnaire. It would be ill conceived to suggest that KDD models could be used as a replacement to clinician- patient interaction but they may aid in the acceleration of some patients for further investigations or ‘straight to test’ if used on those referred as routine patients

Data Mining and Associated Analytical Tools as Decision Aids for Healthcare practitioners in Vascular Surgery

Vascular surgery is an increasingly data rich speciality. Planning treatment and assessing outcomes are highly dependent on objective assessment of number of imaging modalities including duplex ultrasound, CT scans and angiograms which are almost exclusively digitally created stored and accessed. Developments such as the national vascular registry mean that treatment outcomes are recorded scrutinised electronically. The widespread availability of data which is collected electronically and stored for future clinical use has created the opportunity to examine the efficacy of investigations and treatments in a way which has hitherto not been possible. In addition, new computational methods for data analysis have provided the opportunity for the clinicians and researchers to utilise this data to address pertinent clinical questions

Wearable Sensors as a Preoperative Assessment Tool: A Review

Surgery is a common first-line treatment for many types of disease, including cancer. Mortality rates after general elective surgery have seen significant decreases whilst postoperative complications remain a frequent occurrence. Preoperative assessment tools are used to support patient risk stratification but do not always provide a precise and accessible assessment. Wearable sensors (WS) provide an accessible alternative that offers continuous monitoring in a non-clinical setting. They have shown consistent uptake across the perioperative period but there has been no review of WS as a preoperative assessment tool. This paper reviews the developments in WS research that have application to the preoperative period. Accelerometers were consistently employed as sensors in research and were frequently combined with photoplethysmography or electrocardiography sensors. Pre-processing methods were discussed and missing data was a common theme; this was dealt with in several ways, commonly by employing an extraction threshold or using imputation techniques. Research rarely processed raw data; commercial devices that employ internal proprietary algorithms with pre-calculated heart rate and step count were most commonly employed limiting further feature extraction. A range of machine learning models were used to predict outcomes including support vector machines, random forests and regression models. No individual model clearly outperformed others. Deep learning proved successful for predicting exercise testing outcomes but only within large sample-size studies. This review outlines the challenges of WS and provides recommendations for future research to develop WS as a viable preoperative assessment tool